Electrocardiographic Abnormalities and Cardiovascular Disease Risk in Type 1 Diabetes: The Epidemiology of Diabetes Interventions and Complications (EDIC) Study
Bibliographic record
Abstract
OBJECTIVE: We examined the association between the prevalence and incidence of electrocardiographic (ECG) abnormalities and the development of cardiovascular disease (CVD) in patients with type 1 diabetes, among whom these ECG abnormalities are common. RESEARCH DESIGN AND METHODS: We conducted a longitudinal cohort study involving 1,306 patients with type 1 diabetes (mean age 35.5 ± 6.9 years; 47.7% female) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study. ECG abnormalities were defined by the Minnesota Code ECG classification as major, minor, or no abnormality. CVD events were defined as the first occurrence of myocardial infarction, stroke, confirmed angina, coronary artery revascularization, congestive heart failure, or death from any CVD. RESULTS: During a median follow-up of 19 years, 155 participants (11.9%) developed CVD events. In multivariable Cox proportional hazard models adjusted for demographics and potential confounders, the presence of any major ECG abnormalities as a time-varying covariate was associated with a more than twofold increased risk of CVD events (hazard ratio [HR] 2.10 [95% CI 1.26, 3.48] vs. no abnormality/normal ECG, and 2.19 [1.46, 3.29] vs. no major abnormality). Also, each visit (year) at which the diagnosis of major ECG abnormality was retained was associated with a 30% increased risk of CVD (HR 1.30 [95% CI 1.14, 1.48]). The presence of minor ECG abnormalities was not associated with a significant increase in CVD risk. CONCLUSIONS: The presence of major ECG abnormalities is associated with an increased risk of CVD in patients with type 1 diabetes. This suggests a potential role for ECG screening in patients with type 1 diabetes to identify individuals at risk for CVD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".